We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, recent work has proposed machine learning (ML)-based preference elicitation algorithms that aim to elicit only the most critical information from bidders to maximize efficiency. However, while the SOTA ML-based algorithms elicit bidders' preferences via value queries, ICAs that are used in practice elicit information via \emph{demand queries}. In this paper, we introduce a novel ML algorithm that provably makes use of the full information from both value and demand queries, and we show via experiments that combining both query types results in significantly better learning performance in practice. Building on these insights, we present MLHCA, a new ML-powered auction that uses value and demand queries. MLHCA significantly outperforms the previous SOTA, reducing efficiency loss by up to a factor 10, with up to 58\% fewer queries. Thus, MLHCA achieves large efficiency improvements while also reducing bidders' cognitive load, establishing a new benchmark for both practicability and efficiency. Our code is available at https://github.com/marketdesignresearch/MLHCA.

翻译：我们研究迭代组合拍卖（ICAs）的设计。该领域的主要挑战在于，组合空间随物品数量呈指数增长。为解决这一问题，近期研究提出了基于机器学习（ML）的偏好启发算法，旨在仅从竞拍者处获取最关键信息以最大化效率。然而，尽管现有的最先进（SOTA）ML算法通过价值查询（value queries）启发竞拍者偏好，实践中使用的ICAs却通过需求查询（demand queries）获取信息。本文提出了一种新颖的ML算法，该算法可证明地充分利用了价值查询与需求查询的完整信息，并通过实验表明，在实践中结合两种查询类型能显著提升学习性能。基于这些洞见，我们提出了MLHCA——一种使用价值查询与需求查询的新型ML驱动拍卖。MLHCA显著优于先前SOTA，将效率损失降低至十分之一，同时查询次数减少高达58%。因此，MLHCA在实现大幅效率改进的同时减轻了竞拍者的认知负荷，为实用性与效率树立了新标杆。我们的代码已开源，见https://github.com/marketdesignresearch/MLHCA。